topsis-102217126 1.0

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TOPSIS Method Implementation

This Python program implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, which is used for multi-criteria decision analysis. It helps in evaluating and ranking alternatives based on several criteria and weights.

Requirements

• Python 3.x
• Required libraries:
  • pandas
  • numpy

You can install the necessary libraries using:

pip install pandas numpy

Functionality

The program performs the following tasks:

1. Input Validation: It validates the input CSV file, the weights, and the impacts.
2. Normalization: It normalizes the decision matrix using vector normalization.
3. Weighted Normalization: Each attribute of the normalized decision matrix is multiplied by its corresponding weight.
4. Ideal and Negative Ideal Solutions: It computes the ideal and negative ideal solutions based on the given impacts (+ for beneficial, - for non-beneficial).
5. Distance Calculation: The program calculates the distance of each alternative from the ideal and negative ideal solutions.
6. TOPSIS Score Calculation: A score is computed for each alternative, which indicates its closeness to the ideal solution.
7. Ranking: The alternatives are ranked based on their scores, with the best alternative ranked 1.
8. Output: The results (scores and rankings) are saved in a CSV file.

Usage

Command-Line Usage

The program is run through the command line with the following parameters:

topsis <inputFileName> <Weights> <Impacts> <resultFileName>

Parameters

• <inputFileName>: Path to the input CSV file containing the decision matrix. The first column should contain the alternatives, and the subsequent columns should contain the criteria.
• <Weights>: Comma-separated list of weights for each criterion (e.g., 0.25,0.35,0.4).
• <Impacts>: Comma-separated list of impacts for each criterion (either + for beneficial or - for non-beneficial, e.g., +, -, +).
• <resultFileName>: Path to the output CSV file where the results will be saved. This file will contain the score and rank for each alternative.

Example

python topsis.py data.csv "0.4,0.3,0.3" "+,-,+" result.csv

This will:

1. Read data from data.csv
2. Use weights 0.4, 0.3, 0.3 for the criteria
3. Apply impacts +, -, + to the criteria
4. Save the results (including the calculated scores and ranks) to result.csv

Input File Format

The input CSV file should have the following format:

Alternative	Criterion 1	Criterion 2	Criterion 3
A1	3.5	2.1	8.4
A2	4.2	1.9	7.5
A3	5.1	2.8	6.2

• The first column should contain the names of the alternatives.
• The remaining columns should contain the values of the criteria for each alternative.

Output File Format

The output CSV file will contain the following columns:

Alternative	Criterion 1	Criterion 2	Criterion 3	Score	Rank
A1	3.5	2.1	8.4	0.825	1
A2	4.2	1.9	7.5	0.713	2
A3	5.1	2.8	6.2	0.665	3

• Score: The computed score indicating the proximity to the ideal solution.
• Rank: The rank based on the score.

Error Handling

• File Not Found: If the input file does not exist, a FileNotFoundError is raised.
• Invalid Weights or Impacts: If the number of weights does not match the number of impacts, or if the impacts contain invalid values, a ValueError will be raised.
• General Errors: Any other issues during execution will be caught and displayed as an error message.

License

This program is open-source and released under the MIT License. Feel free to use and modify it for your needs.

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